Educational technology is widely used in higher education. The implementation requires the integration of learning systems. The integrated systems can be evaluated to see if they assist learners in achieving learning outcomes by interacting with relevant content. The study focused on inferential statistics arising from the technology acceptance evaluation of a software design model. The proposed software design model automatically combines data to integrate a Learning Management System (LMS) with Massive Open Online Courses (MOOCs). The inferential statistics explain the acceptance or rejection levels of the software design model by the stakeholders (lecturers, students, and universities at large) based on the Technology Acceptance Model and Task-Technology Fit. Google forms were used to obtain information on the software design model. Partial least squares structural equation modelling was applied for data analysis because the data were non-normally distributed. The study’s results showed that Task-Technology Fit constructs had a significant effect on the technology acceptance model. The three constructs inﬂuencing use that emerged from this study were, in order of importance: perceived usefulness, perceived ease of use, and intention to adopt. Perceived usefulness was the most powerful construct due to its total effect size, proving the importance of useful technology in the higher education setting.